Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
High-Performance Video Ingestion with Async Python
Video is one of the most demanding data types to process. In this workshop, you'll learn how to build high-performance video ingestion pipelines using Python's async capabilities. We'll cover asyncio fundamentals for I/O-bound video processing, concurrent frame extraction and processing, async queue patterns for data pipelines, performance profiling and optimization, and real-world deployment considerations. Build a production-grade async video ingestion system from scratch.
From Voice to Action: Building an AI Assistant with Python and Google Workspace
Jumping between Gmail, Calendar, Drive, and Jira tabs for repetitive tasks is exhausting. That's why we built Attento, an assistant that lets you execute real actions in Google Workspace using natural language. In this talk we build Attento, an end-to-end voice assistant that turns natural language into real actions across Google Workspace. We'll cover architecture with FastAPI, OAuth 2.0 authentication with PKCE, function calling with Gemini, streaming with NDJSON, best practices with uv and Pydantic Settings, and the path from demo to production with Postgres and automated morning briefings.
Juan Manuel Marín Bedoya
Senior Data Engineer @ Huge
Juliana Suárez Ávila
Data Scientist @ Cuesta Partners
Leverage your Python skill using the Python interpreter
In this talk, I'll challenge the audience's mindset about Python. Python is not an interpreter, and in fact, there are multiple Python interpreters—each with its own architecture and purpose. I'll walk through Python's core internals and show how programming languages interact beneath the surface. We'll explore how to write better Python by understanding the garbage collector, what you can build using the AST, how to read and leverage the disassembler, and the practical implications of Python's transition from its old LL(1) parser to the current PEG parser. We'll also dive into lesser-known features of Python interpreters, what a PEP really is and how it shapes the language, and conclude with a deep look at Python without the GIL—what changes, what breaks, and how the core team removed it. Throughout the talk, I'll share personal stories, including battles caused by identical ASTs and the moment I believed I had discovered a way to speed up the Python interpreter itself.
Building Your First AI Tool Server: Creating a Pokédex with FastMCP and Python
Build your first AI tool server from scratch using FastMCP and Python, with the Pokédex as your guide! In this hands-on workshop, you'll learn the Model Context Protocol (MCP), set up a FastMCP server, implement custom tools that AI agents can call, and connect everything into a working Pokédex AI assistant. No prior MCP experience needed—just Python knowledge and a love for Pokémon.
From S3 to AI Agent: Your First Queryable Lakehouse
AI agents are only as good as the data they can query. Most agents built today connect to outdated CSVs, unstructured databases, or nothing at all. What if your agent could query a real lakehouse—with versioning, schema evolution, and time travel—using natural language? In this workshop we build exactly that from scratch using only open-source tools that run on your laptop. Starting from a local Docker Compose stack, we stand up a functional lakehouse with MinIO as S3-compatible storage, Apache Iceberg as the table format, Project Nessie as a Git-like versioned catalog, and Trino as the SQL query engine. On top of that, we build a Python MCP server that exposes Iceberg tables as tools for an AI agent, and connect Claude so it can query the lakehouse in natural language.
Provenance by Default: AI Media Pipelines in Python
A model can now generate a video that looks indistinguishable from one your camera recorded. The same is true for an image, a voice, or a song. As Python developers, we are building those pipelines — and we are also the ones who will be asked, very soon, to prove what came out of them. This talk is about building generative media pipelines in Python in a way that answers that question by default. We'll walk through Genblaze, an open-source SDK (github.com/backblaze-labs/genblaze, MIT licensed) that I work on at Backblaze, and use it as a vehicle to talk about the design problems any team faces when wiring AI generation into a real product. We will cover, with live code: the Pipeline pattern with a fluent Pipeline → Step → Run → Manifest API built on Pydantic v2; one API across eleven providers; provenance that survives the file with SHA-256-verified manifests embedded into PNG, JPEG, MP4, MP3, and WAV; privacy and policy controls; storage and replay; and agent loops with lineage. By the end, attendees will have a clear reference for how to architect generative-AI features in Python so that what did this system actually produce, and can I prove it? is a one-line answer instead of a ticket.
NLP Without Labels: How to Cluster N Legal Processes of the Colombian State and Turn Chaos into a Production Classifier
What do you do when you have 600,000 legal complaints, zero labeled data, and a government entity waiting for results? This talk walks through the full process of building an unsupervised NLP classification system for the Procuraduría General de la Nación. Starting from raw administrative text—noisy, full of abbreviations and institutional jargon—I'll show how TF-IDF, truncated SVD, and KMeans combined to organize more than half a million records into 64 semantically coherent groups, without a single manual label. But clustering is only the starting point. I'll cover how clusters were validated, how a Logistic Regression classifier was trained on them to make the system deployable, and how the final pipeline was packaged in a .pkl that non-technical colleagues use in production today. Along the way we'll face real problems: elbow curves that don't behave, 1:20 size imbalances between clusters, and the tension between mathematical elegance and institutional usability. Because in the public sector, a model nobody uses isn't a model—it's a PDF gathering dust.
Python in the Browser: Powered by WebAssembly
What if the browser could run Python as a first-class language? In this talk, I'll show how PyScript makes it possible to execute real Python directly in the browser, powered by WebAssembly. Through a series of exciting, live examples, you'll see Python manipulating the DOM, calling browser APIs, and building interactive experiences, all without a traditional JavaScript codebase. I will also show a couple of examples of how you can embed both JavaScript and Python on PyScript to make even more exciting tools. I will also discuss what WebAssembly is, why it exists, and how it enables languages like Python to run safely and efficiently on the web platform. Finally, I'll discuss when tools like PyScript make sense, and compare it with similar tools. Whether you're a Python developer curious about the frontend, an engineer interested in WebAssembly, or simply someone who enjoys seeing the boundaries of Python pushed, this talk will change how you think about what can run in a browser.
Elevate your code quality in Python with modern, ultra-fast tooling
AI coding assistants have changed how we build software. We can now generate features, refactors, and entire services in minutes — but speed without strong engineering practices quickly becomes technical debt. In this talk, I'll show how modern Python teams can build fast and reliable development workflows using tools like Astral's Ruff, Ty, and uv. We'll explore how traditional slow and noisy quality pipelines are being replaced by a new generation of tooling that provides near-instant feedback while improving code quality and developer experience. Topics include why AI-generated code makes automated quality gates more important than ever, using Ruff for formatting and linting, using Ty for modern static typing, structuring formatter → linter → type-checker workflows, pre-commit hooks and CI pipelines developers actually enjoy using, and reducing friction between local development and CI/CD.
Clean Code in the Era of LLMs: Do Good Practices Still Matter?
Instead, research from METR, CodeRabbit, and GitClear is converging on an uncomfortable truth: code duplication has quadrupled, copy-pasted code now exceeds moved code, bugs have risen 70%, and security issues have nearly tripled. AI didn't break our codebases. It amplified what was already broken. So what do we actually do about it? This talk makes the case that clean code, SOLID, DDD, TDD, and design patterns matter more than ever when LLMs write half the code. Your codebase is now a prompt: clean code leads to better AI suggestions, which make it easier to stay clean. We'll walk through which practices now matter more, which ones have quietly turned against you, and how to collaborate with an LLM without becoming a rubber stamp for its output. You'll leave with a concrete framework, Adversarial Collaboration: generate, critique, refactor, verify. Not vibe coding. Real engineering, just faster.
From Prompts to Agents: Intelligent Systems with Python
Take your first steps from writing simple prompts to building intelligent multi-agent systems with Python. In this beginner-friendly workshop, you'll learn the foundations of AI agents, how they differ from simple LLM calls, how to chain agents together for complex tasks, and how to give them tools and memory. Using popular Python frameworks, you'll build a working multi-agent system by the end of the session—no prior AI experience needed.
Understanding Cognitive Complexity in Python
Modern Python makes it incredibly easy to write code quickly, but much harder to keep it understandable as projects grow. This talk explores cognitive complexity: a metric focused not on what code does, but on how difficult it is for humans to read, reason about, and maintain. Through real Python examples, we will analyze how nested conditionals, branching logic, async flows, exceptions, and growing business rules silently increase the mental load required to work with a codebase. We will also discuss why traditional metrics such as cyclomatic complexity often fail to reflect actual readability, and how cognitive complexity provides a more human-centered perspective on maintainability. The talk includes practical refactoring techniques, common anti-patterns found in production Python projects, and lessons learned while building complexipy, an open source cognitive complexity analyzer for Python written in Rust, designed to provide fast local feedback and CI integration.
Your AI Eval Is Lying To You
When you set temperature=0 and run your AI eval, you expect the same input to give the same output. It doesn't. Recent measurements on Qwen3-235B at temperature=0 produced 80 unique completions on a single prompt. So when your eval reports "92% pass rate," what does that actually mean? This talk is about the gap between how the AI eval ecosystem talks about scores and what those scores can actually support. We walk through five specific tools that fix the gap: Pass@k versus pass^k, Wilson confidence intervals, Bayesian pass@k with Beta-Binomial conjugacy, sequential drift detection with EWMA, CUSUM, and OLS, and family-wise error control via Benjamini-Hochberg procedures. Each method gets a short demo in pure Python with no framework dependency. The audience leaves with reference implementations they can paste into an existing pytest setup tonight.
Beyond Vibe Coding: Spec Driven Development with Code Graphs
Go beyond vibe coding and learn how to use specifications and code graphs to guide AI-assisted development. In this workshop, you'll discover how structured specs and dependency graphs give AI coding tools the context they need to produce coherent, maintainable code. We'll work with real Python projects to define specs, generate code graphs, and wire them into your AI-assisted workflow—resulting in code that actually makes sense architecturally.
Esneider Bravo Benítez
Software Engineer @ Lendingfront
Jonathan Vallejo Muñoz
Software Engineer @ Lendingfront
Stop Mocking, Start Containerizing
Tired of maintaining brittle mock objects that don't reflect production behavior? In this workshop, you'll learn how to replace mocks with real containerized services using Testcontainers for Python. Bring your laptop and a running Docker engine—we're going to get our hands dirty!
Now or Never! Token Diet with TOON to Save Money and Help AI Understand More
Tokens cost money, and every unnecessary token you send to an LLM is money wasted. In this workshop, you'll learn how to put your AI applications on a token diet using TOON, a Python tool for creating compact, semantically rich data representations. We'll cover TOON's architecture, how to serialize complex data structures efficiently, measure token reduction, and integrate TOON into existing AI pipelines—without losing the information your models need.
